Chitinase-3-Like Protein 1, Serum Amyloid A1, C-Reactive Protein, and Procalcitonin Are Promising Biomarkers for Intracranial Severity Assessment of Traumatic Brain Injury: Relationship with Glasgow Coma Scale and Computed Tomography Volumetry

Carabias, Cristina Sánchez; Gomez, Pedro A.; Panero, Irene; Eiriz, Carla; Castaño‐León, Ana M.; Egea, Javier; Lagares, Alfonso; Paredes, Igor; Alén, José A.; Moreno-Gómez, Luis Miguel; García‐Pérez, Daniel; Chico‐Fernández, Mario; Barea‐Mendoza, Jesús Abelardo

doi:10.1016/j.wneu.2019.09.143

Cited by 22 publications

(28 citation statements)

References 60 publications

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“…Third, the XGBoost model showed that the GCS score, PCT, the Marshall CT score, CRP, and long-term sedation potentially predicted ARF in patients with M-STBI. Consistent with previous reports, the GCS score, PCT, and CRP were related to ARF in patients with M-STBI, suggesting the extent of TBI and severity of systematic inflammation (26)(27)(28)(29). The GCS was center factor in the XGBoosting model shown in Figure 2, suggesting that the severity of brain injury was associated with ARF in patients with M-STBI significantly.…”

Section: Discussionsupporting

confidence: 88%

Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury

Bai

et al. 2021

Front. Med.

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Background: There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes. This study aimed to design a predictive model for ARF.Methods: Adult patients with M-STBI [3 ≤ Glasgow Coma Scale (GCS) ≤ 12] with a definite history of brain trauma and abnormal head on CT images, obtained from September 2015 to May 2017, were included. Patients with age >80 years or <18 years, multiple injuries with TBI upon admission, or pregnancy (in women) were excluded. Two models based on machine learning extreme gradient boosting (XGBoost) or logistic regression, respectively, were developed for predicting ARF within 48 h upon admission. These models were evaluated by out-of-sample validation. The samples were assigned to the training and test sets at a ratio of 3:1.Results: In total, 312 patients were analyzed including 132 (42.3%) patients who had ARF. The GCS and the Marshall CT score, procalcitonin (PCT), and C-reactive protein (CRP) on admission significantly predicted ARF. The novel machine learning XGBoost model was superior to logistic regression model in predicting ARF [area under the receiver operating characteristic (AUROC) = 0.903, 95% CI, 0.834–0.966 vs. AUROC = 0.798, 95% CI, 0.697–0.899; p < 0.05].Conclusion: The XGBoost model could better predict ARF in comparison with logistic regression-based model. Therefore, machine learning methods could help to develop and validate novel predictive models.

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Section: Discussionsupporting

confidence: 88%

Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury

Bai

et al. 2021

Front. Med.

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“…Both sCD14 and Ch3L1 are considered to be microglial markers, which coincides with our data that microglial activation has a central role in the physiopathology of MPS ( 29 , 30 ). Ch3L1 (in humans also named YKL-40) can be assumed as an almost universal marker of diseases: for neural pathologies, it has been suggested as a biomarker of multiple sclerosis ( 31 ), traumatic brain injury ( 32 ), Alzheimer's disease ( 33 ), amyotrophic lateral sclerosis ( 34 ), iatrogenic and sporadic Creutzfeldt-Jakob disease ( 35 ), or bipolar disorder ( 36 )—not to mention various pathologies in peripheral organs.…”

Section: Discussionmentioning

confidence: 99%

Surrogate Cerebrospinal Fluid Biomarkers for Assessing the Efficacy of Gene Therapy in Hurler Syndrome

et al. 2021

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Mucopolysaccharidosis type I (MPS I) is caused by a deficiency of the lysosomal hydroxylase alpha-l-iduronidase (IDUA). The resulting accumulation of dermatan and heparan sulfate induces intellectual disabilities and pre-mature death, and only a few treatment options are available. In a previous study, we demonstrated the feasibility, safety, and efficacy of gene therapy by injecting recombinant adeno-associated viral vector serotype (AAV)2/5-IDUA into the brain of a canine model of MPS I. We report on a quantitative proteomic analysis of control dogs and untreated dogs with MPS I cerebrospinal fluid (CSF) that had been collected throughout the study in the MPS I dogs. Mass spectrometry (MS) analysis identified numerous proteins present at altered levels in MPS I CSF samples. Quantitative immunoblotting, performed on CSF from healthy controls, untreated MPS I dogs, and MPS I dogs early treated and late treated by gene therapy, confirmed the MS data for a subset of proteins with higher abundance (neuronal pentraxin 1, chitinase 3-like 1, monocyte differentiation antigen CD14, and insulin-like growth factor-binding protein 2). Scoring of the results shows that the expression levels of these proteins are close to those of the control group for dogs that underwent gene therapy early in life but not for older treated animals. Our results disclose four novel predictive biomarker candidates that might be valuable in monitoring the course of the neurological disease in MPS patients at diagnosis, during clinical follow-up, and after treatment.

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“…Interestingly, PCT was higher in children with BSC<3 than those with BCS>2 (P=0.004) and showed moderate predictive value for coma with an AUROCC of [0.65] which is to our knowledge a novel nding. Of note, PCT in combination with CRP, chitinase-3-like-protein, and S100beta was recently described to be a promising biomarker in determining the presence, location, and extent of traumatic intracranial lesions [39] The level of PTX3 was higher in children with SM or CM compared to children with UM, in children with BCS<3 compared to those with BCS >2 and in children who died compared to those who survived with signi cant diagnosis performance for severity, coma and mortality as shown by the signi cance of the AUROCC analyses (Figure :4D,5A,6B).…”

Section: Discussionmentioning

confidence: 99%

Procalcitonin, Pentraxine 3, and Molecules of the Vascular Endothelium are Hallmark of Pathogenicity and Predict Coma and Mortality Among Children with Severe and Cerebral Malaria

Tornyigah

Blankson

Adamou³

et al. 2021

Preprint

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Background: Malaria-related deaths could be prevented if prompt diagnosis and prognostic biomarkers are available to allow rapid adequate treatment. Understanding of the mechanisms implicated in the progression from P. falciparum asymptomatic and uncomplicated malaria infections to severe life-threatening disease is necessary to identify such indicators. Methods: Using quantitative ELISA, we assessed the plasma concentrations of Procalcitonin, Pentraxine 3, Ang-2, sTie-2, suPAR, sEPCR, and sICAM-1 in a cohort of 337 Beninese children who presented with pediatric malaria to investigate the potential association with clinical manifestations and outcomes. Results: The molecules showed higher levels in children with severe or cerebral malaria compared to those with uncomplicated malaria. Plasmas concentrations of PTX3, PCT and the soluble receptors suPAR, sTie-2, sICAM-1 were significantly higher in children with deep coma as defined by a Blantyre Coma Score < 3 (P <0.001 for PTX3, suPAR, sTie-2, p=0.004 for PCT and p=0.005 for sICAM-1). Moreover, the concentrations of PTX3, suPAR and sEPCR were higher among children who died from severe malaria compared to those who survived with (p=0.037, p=0.035, and p=0.002 respectively).Conclusion: Our findings indicate the ability of these seven bioactive molecules to discriminate among the clinical manifestations of malaria and therefore, given their potential utility as prognostic biomarkers for severe and fatal malaria, they might be useful to improve severe cases management.

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Chitinase-3-Like Protein 1, Serum Amyloid A1, C-Reactive Protein, and Procalcitonin Are Promising Biomarkers for Intracranial Severity Assessment of Traumatic Brain Injury: Relationship with Glasgow Coma Scale and Computed Tomography Volumetry

Cited by 22 publications

References 60 publications

Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury

Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury

Surrogate Cerebrospinal Fluid Biomarkers for Assessing the Efficacy of Gene Therapy in Hurler Syndrome

Procalcitonin, Pentraxine 3, and Molecules of the Vascular Endothelium are Hallmark of Pathogenicity and Predict Coma and Mortality Among Children with Severe and Cerebral Malaria

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